The present invention relates to a method of extracting desired features from a cellular image. This extraction method is particularly good at extracting relatively weakly defined features from relatively noisy images, such as faults or geologic horizons from 2D or 3D seismic data.
While the inventive method has been designed to extract faults from seismic attribute data, it is generally applicable to all kinds of line and surface extraction problems. Extraction of faults from attributes is nontrivial due to the characteristics of the data and the desire to extract intersecting faults as separate objects. This patent application shows how faults, which are often represented by poorly defined and weakly connected ridges, may be extracted by the cooperation of a swarm (typically thousands) of intelligent agents. Knowledge about the properties of a typical fault is encoded in the agents, and each agent is intended to extract a small segment of the fault. Each segment is stored as one object along with its inferred local properties. The faults are expected to be completely covered by the agents, and the extracted segments having comparable properties are then merged into complete faults. The use of thousands of agents searching for specific local properties from different starting points makes this a very robust approach, which can work under very difficult conditions. The approach is well suited for solving problems in applications where the desired structures are more trends than continuous ridges in a noisy environment.
Segmentation of ridgelines and surfaces in 2D and 3D data has many applications, and much work has been done within this field. Examples of heavily studied 2D applications are mapping of roads, railroads and rivers from high-resolution satellite images and analysis of blood cells in medical data. 3D surface and object reconstruction has applications in, for instance, extracting the bones in the skull from tomography and imaging of dental data. Many of the approaches developed to solve these kinds of tasks are designed to deal with the presence of noise in the images, but they nevertheless require that the extrema surfaces or lines comprise a “ridge” which is well defined and fairly continuous. They usually strive for generality, i.e. they make few or no assumptions on the shape of the surfaces/lines to be extracted.
The inventive method addresses the problem of extracting lines from 2D data and surfaces from 3D data in a rather “tough” environment. The following assumptions are made regarding the input data:                The extrema lines or surfaces often lack continuity and are weakly defined;        Making the extrema lines or surfaces more continuous by smoothing requires a large filter that will introduce unacceptable errors and mask desired details;        It is nontrivial to locally estimate the exact shape and extent of the entire line or surface;        Intersecting lines or surfaces are to be extracted as different objects;        Only structures having some expected properties are to be extracted, not other structures that may be present in the data; and        The data has a high level of noise.        
These assumptions represent poor conditions for a conventional line/surface extraction algorithm. In order to deal with the lack of continuity, the intersecting objects, the high noise level, and the presence of unwanted structures, the inventive method utilises prior knowledge of which line or surface properties to look for. That is, a specialised algorithm is needed which extracts only the lines or surfaces exhibiting expected characteristics. Even though this patent application will focus on segmentation where knowledge about the desired structure is utilised, the method itself is general, and can be used on the same problems as conventional line and surface extraction algorithms.
It is an object of the present invention to provide an improved method of extracting desired features from a cellular image, and particularly a method for extracting relatively weakly defined features from relatively noisy images, such as faults or geologic horizons from 2D or 3D seismic data.